Stakeholder Engagement Programs: Balancing International Best Practices and In-Country Leadership
Bibliographic record
Abstract
Abstract Successful development of international exploration and production (E&P) projects is increasingly dependent on managing social and stakeholder concerns. Critical to this success is a balance between the need for international best practices and local leadership of stakeholder engagement (SE) programs. As international practitioners, we see the following critical trends emerging: There has been significant progress in developing SE best practices/methods in recent years;Few host country social experts have been exposed to these developing practices/methods; andDespite this, experts from within host countries are typically in the best position to execute these programs because they: ◦Understand the social customs, language, and culture◦Are known and more trusted by regulators and residents The authors argue that to better manage this balance, international oil and gas companies and their consultants need to commit to two-way capacity building that simultaneously: Conveys best practices from international oil companies, financial institutions and ESHIA practitioners to capable local consultants; andCommunicates to international parties and practitioners the value of stakeholder engagement being led by qualified local experts. Through a series of case studies, this paper demonstrates how the challenge of effectively managing international SE programs can impact development projects and how the recommended approach can help meet this challenge. The paper provides conclusions and recommendations for how to improve capacity building activities and optimize opportunities for local involvement in stakeholder consultation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.071 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".